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In this paper we apply the Multi-Look Joint Sparsity Fusion algorithm to multisensor image data. Our algorithm at first performs sparse unmixing of the hyperspectral data and selects pixels for a second unmixing of the multispectral image. This is done by applying a joint sparsity model, which exploits similarities within neighbouring pixels. We test our resolution enhancement method using a hyperspectral...
Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models. In essence, this approach exploits the fact that HSI pixels can be associated with a small number of constituent pure materials. However, unlike traditional least-squares-based methods, sparsity-based techniques do not require a preselection of endmembers and are thus able...
Sparse spectral unmixing can be modeled as a linear combination of endmembers contained in an overcomplete dictionary weighted by the corresponding sparse abundance vector. This method exploits the fact that there is only a small number of endmembers inside a pixel compared to the overcomplete endmember spectral dictionary. Since the information contained in hyperspectral pixels is often spatially...
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